Decision support for the software product line domain engineering lifecycle |
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Authors: | Ebrahim Bagheri Faezeh Ensan Dragan Gasevic |
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Affiliation: | 1.School of Computing and Information Systems,Athabasca University,Athabasca,Canada;2.Sauder School of Business,University of British Columbia,Vancouver,Canada |
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Abstract: | Software product line engineering is a paradigm that advocates the reusability of software engineering assets and the rapid
development of new applications for a target domain. These objectives are achieved by capturing the commonalities and variabilities
between the applications of the target domain and through the development of comprehensive and variability-covering feature
models. The feature models developed within the software product line development process need to cover the relevant features
and aspects of the target domain. In other words, the feature models should be elaborate representations of the feature space
of that domain. Given that feature models, i.e., software product line feature models, are developed mostly by domain analysts
by sifting through domain documentation, corporate records and transcribed interviews, the process is a cumbersome and error-prone
one. In this paper, we propose a decision support platform that assists domain analysts throughout the domain engineering
lifecycle by: (1) automatically performing natural language processing tasks over domain documents and identifying important
information for the domain analysts such as the features and integrity constraints that exist in the domain documents; (2) providing
a collaboration platform around the domain documents such that multiple domain analysts can collaborate with each other during
the process using a Wiki; (3) formulating semantic links between domain terminology with external widely used ontologies such
as WordNet in order to disambiguate the terms used in domain documents; and (4) developing traceability links between the
unstructured information available in the domain documents and their formal counterparts within the formal feature model representations.
Results obtained from our controlled experimentations show that the decision support platform is effective in increasing the
performance of the domain analysts during the domain engineering lifecycle in terms of both the coverage and accuracy measures. |
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